RIS-aided communication and localization have been intensely studied in recent years, see, e.g., [1]}, [2]}, [3]}, [4]}, [5]}. In the RIS-assisted communications context, for instance, a location-aware beamforming design in the far-field (FF) region was proposed in [1]}, where authors established the geometrical channel model based on the user's 3D location. In the localization context, the 3D position of a user with the aid of RIS has been estimated in [2]}, where the authors have considered the FF model. For 3D user localization in the near-field (NF) region of an RIS acting as a lens, a low-complexity estimator has been proposed in [3]}. For 2D RIS-aided user positioning, the RIS-assisted received signal strength (RSS)-based localization algorithms have been proposed in [4]}. In [5]}, the linear RIS phase profile has been designed for both communications and positioning systems for the FF region in a 2D scenario.
According to the recent works in RIS-aided localization systems, it can be realized that the location and orientation of the RIS need to be known. That is because RIS is one of the reference points in these systems [11]}. In the context of RIS-aided communication systems, knowing RIS location can facilitate the location-based RIS phase profile optimization [12]}. However, the location and the orientation of the RIS were assumed to be known in all previous studies, which is non-trivial to obtain in practice. The problem of RIS localization (i.e., determining the position of an RIS) can be seen as a bi-static sensing problem, and was previously tackled in [13]} under FF conditions. However, the RIS orientation could not be determined, given the time of arrival (TOA) measurements. When the RIS is large, devices in most RIS-aided systems will be in the NF, which makes the RIS localization problem more challenging than in [13]}.
